AI 中文总结
该研究针对区间数据同化的观测设计问题，提出AQD-IDA方法，通过联合选择观测目标与阈值设计二元查询，在固定预算下提升了Lorenz-96等模型的同化精度，凸显主动设计粗观测信息的价值。
AI 中文摘要
数据同化通过结合模型预报与观测来估计动力系统的演化状态。尽管许多传统方法假设测量值为点值，但实际传感系统可能提供粗粒度信息，如二元、有序、不等式或区间值报告。区间数据同化（Interval Data Assimilation, IDA）提供了同化此类信息的原则性框架，但假设待同化的观测已预先指定。然而，在许多传感场景中，既可以选择查询位置，也可以选择查询何种不等式，且仅能发出有限数量的查询。这提出了一个新的观测设计问题：在查询响应未知时，应如何从预报不确定性中选择信息丰富的不等式查询？我们提出区间数据同化主动查询设计（Active Query Design for Interval Data Assimilation, AQD-IDA），该方法在IDA更新前利用预报集合设计二元阈值查询。AQD-IDA将观测目标和定义不等式的阈值均视为设计变量，从而使同化系统不仅能确定观测位置，还能确定查询内容。我们开发了考虑预报不确定性、查询间冗余以及后验不确定性预期减少的查询选择准则。在Lorenz-96和球型准地转模型上的实验表明，在固定二元查询预算下，自适应查询设计可提高同化精度，且联合观测目标-阈值选择优于固定或单独设计的查询。这些结果证明，主动设计提供给数据同化的信息可大幅提升粗粒度观测的价值。
英文摘要
Data assimilation estimates the evolving state of a dynamical system by combining model forecasts with observations. While many conventional methods assume point-valued measurements, practical sensing systems may instead provide coarse information such as binary, ordinal, inequality, or interval-valued reports. Interval Data Assimilation (IDA) provides a principled framework for assimilating such information, but assumes that the observations to be assimilated are specified in advance. In many sensing settings, however, both where to query and what inequality to ask can be chosen, while only a limited number of queries can be issued. This raises a new observation-design problem: how should informative inequality queries be selected from the forecast uncertainty before their responses are known? We propose \emph{Active Query Design for Interval Data Assimilation} (AQD-IDA), which uses the forecast ensemble to design binary threshold queries prior to the IDA update. AQD-IDA treats both the observation target and the threshold defining the inequality as design variables, thereby allowing the assimilation system to determine not only where to observe but also what question to ask. We develop query-selection criteria that account for forecast uncertainty, redundancy among queries, and anticipated reduction in posterior uncertainty. Experiments on Lorenz--96 and a spherical quasi-geostrophic model show that adaptive query design improves assimilation accuracy under a fixed binary-query budget, with joint observation-target--threshold selection outperforming fixed or separately designed queries. These results demonstrate that actively designing the information supplied to data assimilation can substantially increase the value of coarse observations.
CommentsThis manuscript has been submitted for possible publication in Monthly Weather Review
